Computer Science – Discrete Mathematics
Scientific paper
2012-02-06
Computer Science
Discrete Mathematics
Scientific paper
We have recently proposed a rigorous framework for Uncertainty Quantification (UQ) in which UQ objectives and assumption/information set are brought into the forefront, providing a framework for the communication and comparison of UQ results. In particular, this framework does not implicitly impose inappropriate assumptions nor does it repudiate relevant information. This framework, which we call Optimal Uncertainty Quantification (OUQ), is based on the observation that given a set of assumptions and information, there exist bounds on uncertainties obtained as values of optimization problems and that these bounds are optimal. It provides a uniform environment for the optimal solution of the problems of validation, certification, experimental design, reduced order modeling, prediction, extrapolation, all under aleatoric and epistemic uncertainties. OUQ optimization problems are extremely large, and even though under general conditions they have finite-dimensional reductions, they must often be solved numerically. This general algorithmic framework for OUQ has been implemented in the mystic optimization framework. We describe this implementation, and demonstrate its use in the context of the Caltech surrogate model for hypervelocity impact.
McKerns Michael M.
Ortiz Mauricio
Owhadi Houman
Scovel Clint
Sullivan Timothy John
No associations
LandOfFree
The Optimal Uncertainty Algorithm in the Mystic Framework does not yet have a rating. At this time, there are no reviews or comments for this scientific paper.
If you have personal experience with The Optimal Uncertainty Algorithm in the Mystic Framework, we encourage you to share that experience with our LandOfFree.com community. Your opinion is very important and The Optimal Uncertainty Algorithm in the Mystic Framework will most certainly appreciate the feedback.
Profile ID: LFWR-SCP-O-117914